Detr Finetuned Chess

🚀 DETR (End-to-End Object Detection) model with ResNet-50 backbone fine-tuned on chess pieces

The DETR model is trained end-to-end on a chess pieces recognition dataset, offering a solution for object detection in the context of chess.

🚀 Quick Start

Use the code below to get started with the model:

from transformers import DetrImageProcessor, DetrForObjectDetection
import torch
from PIL import Image
import requests

url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)

processor = DetrImageProcessor.from_pretrained("aesat/detr-finetuned-chess", revision="no_timm")
model = DetrForObjectDetection.from_pretrained("facebook/detr-finetuned-chess", revision="no_timm")

inputs = processor(images=image, return_tensors="pt")
outputs = model(**inputs)

# convert outputs (bounding boxes and class logits) to COCO API
# let's only keep detections with score > 0.9
target_sizes = torch.tensor([image.size[::-1]])
results = processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.9)[0]

for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
    box = [round(i, 2) for i in box.tolist()]
    print(
            f"Detected {model.config.id2label[label.item()]} with confidence "
            f"{round(score.item(), 3)} at location {box}"
    )

✨ Features

The DETR model is an encoder - decoder transformer with a convolutional backbone. Two heads are added on top of the decoder outputs for object detection: a linear layer for class labels and a MLP (multi - layer perceptron) for bounding boxes. It uses object queries to detect objects in an image, with each query looking for a particular object. For COCO, the number of object queries is set to 100.

📚 Documentation

Model Details

Model Description

The DETR model is an encoder - decoder transformer with a convolutional backbone. Two heads are added on top of the decoder outputs in order to perform object detection: a linear layer for the class labels and a MLP (multi - layer perceptron) for the bounding boxes. The model uses so - called object queries to detect objects in an image. Each object query looks for a particular object in the image. For COCO, the number of object queries is set to 100.

Model Sources [optional]

• Repository: [More Information Needed]
• Paper [optional]: [More Information Needed]
• Demo [optional]: [More Information Needed]

Uses

How To Use

The code example above demonstrates how to use the model for object detection without fine - tuning or plugging into a larger ecosystem/app.

Downstream Use [optional]

[More Information Needed]

Out - of - Scope Use

[More Information Needed]

Bias, Risks, and Limitations

[More Information Needed]

Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

Training Details

Training Data

[More Information Needed]

Training Procedure

Preprocessing [optional]

[More Information Needed]

Training Hyperparameters

• Training regime: [More Information Needed]

Speeds, Sizes, Times [optional]

[More Information Needed]

Evaluation

Testing Data, Factors & Metrics

Testing Data

[More Information Needed]

Factors

[More Information Needed]

Metrics

[More Information Needed]

Results

[More Information Needed]

Summary

Model Examination [optional]

[More Information Needed]

Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

Technical Specifications [optional]

Model Architecture and Objective

[More Information Needed]

Compute Infrastructure

Hardware

[More Information Needed]

Software

[More Information Needed]

Citation [optional]

BibTeX:

[More Information Needed]

APA:

[More Information Needed]

Glossary [optional]

[More Information Needed]

More Information [optional]

[More Information Needed]

Model Card Authors [optional]

[More Information Needed]

Model Card Contact

[More Information Needed]